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Libvaxy
0.1
A Terraria library mod focusing on easing development and providing a lot of meta.
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Provides many useful reflection utilities to make it easy. Does automatic caching for certain methods. More...
Static Public Member Functions | |
| static T | GetInstanceField< T > (object obj, string name, BindingFlags flags=AnyPublic) |
| Gets the value of a field inside an instance. More... | |
| static T | GetStaticField< T > (Type type, string name, BindingFlags flags=AnyPublic) |
| Gets the value of a static field in a type. More... | |
| static void | SetInstanceField (object obj, string name, object value, BindingFlags flags=AnyPublic) |
| Sets the value of a field in an instance. More... | |
| static void | SetStaticField (Type type, string name, object value, BindingFlags flags=AnyPublic) |
| Sets the value of a static field in a type. More... | |
| static bool | HasField (Type type, string name) |
| Checks if a given type has a field. More... | |
| static T | GetInstanceProperty< T > (object obj, string name, BindingFlags flags=AnyPublic) |
| Gets the value of a property inside an instance. More... | |
| static T | GetStaticProperty< T > (Type type, string name, BindingFlags flags=AnyPublic) |
| Gets the value of a static property in a type. More... | |
| static void | SetInstanceProperty (object obj, string name, object value, BindingFlags flags=AnyPublic) |
| Sets a property in an instance to the specified value. More... | |
| static void | SetStaticProperty (Type type, string name, object value, BindingFlags flags=AnyPublic) |
| Sets a static property in a type to the specified value. More... | |
| static bool | HasProperty (Type type, string name) |
| Checks whether the given type has a property. More... | |
| static T | InvokeInstanceMethod< T > (object obj, string name, BindingFlags flags=AnyPublic, params object[] parameters) |
| Invokes an instance method and gets its return value. More... | |
| static T | InvokeStaticMethod< T > (Type type, string name, BindingFlags flags=AnyPublic, params object[] parameters) |
| Invokes a static method and gets its return value. More... | |
| static bool | HasMethod (Type type, string name, params Type[] paramTypes) |
| static void | InvokeConstructor (object obj, Type[] ctorParamTypes, params object[] parameters) |
| Invokes a constructor on an object. More... | |
| static bool | HasConstructor (Type type, params Type[] ctorParamTypes) |
| static FieldInfo | GetFieldInfo (Type type, string name, BindingFlags flags=AllFlags) |
| Gets a FieldInfo without internally caching it. More... | |
| static PropertyInfo | GetPropertyInfo (Type type, string name, BindingFlags flags=AllFlags) |
| Gets a PropertyInfo without internally caching it. More... | |
| static MethodInfo | GetMethodInfo (Type type, string name, Type[] paramTypes=null, BindingFlags flags=AllFlags) |
| Gets a MethodInfo without internally caching it. More... | |
| static ConstructorInfo | GetConstructorInfo (Type type, Type[] paramTypes) |
| Gets a ConstructorInfo without internally caching it. More... | |
| static Type[] | GetObjectTypes (params object[] parameters) |
| Gets the type of each parameter passed in. More... | |
| static Type[] | GetParameterTypes (MethodInfo info) |
| static T | CreateInstance< T > () |
| static T | CreateInstance< T > (Type type) |
| static object | CreateInstance (Type type) |
| static Type[] | GetTypesWithAttribute< T > (Assembly assembly=null, bool inherited=false) |
| Searches the given assembly for types holding the specified attribute. More... | |
| static MethodInfo[] | GetMethodsWithAttribute< T > (Assembly assembly=null, bool inherited=false) |
| static Type[] | GetSubtypes (Type type, Assembly assembly=null) |
| static Type[] | GetNonAbstractSubtypes (Type type, Assembly assembly=null) |
Static Public Attributes | |
| const BindingFlags | AllFlags = BindingFlags.Public | BindingFlags.NonPublic | BindingFlags.Instance | BindingFlags.Static |
| const BindingFlags | AnyPublic = BindingFlags.Public | BindingFlags.NonPublic |
| const BindingFlags | AnyInstance = AnyPublic | BindingFlags.Instance |
| const BindingFlags | AnyStatic = AnyPublic | BindingFlags.Static |
Static Private Attributes | |
| static Dictionary< string, FieldInfo > | fieldCache |
| static Dictionary< string, PropertyInfo > | propertyCache |
| static Dictionary< string, MethodInfo > | methodCache |
| static Dictionary< string, ConstructorInfo > | constructorCache |
Provides many useful reflection utilities to make it easy. Does automatic caching for certain methods.
Definition at line 12 of file Reflection.cs.
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static |
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static |
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static |
Gets a ConstructorInfo without internally caching it.
| type | The type containing the property |
| paramTypes | The parameter types of the constructor, in order |
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static |
Gets a FieldInfo without internally caching it.
| type | The type containing the field |
| name | The name of the field |
| flags | Any optional flags you want to pass in. By default, most flags are automatically included |
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inlinestatic |
Gets the value of a field inside an instance.
| T | The type that the field value should be automatically cast to |
| obj | The instance containing the field |
| name | The name of the field |
| flags | Any optional flags you want to pass in. BindingFlags.Instance is always automatically included |
Definition at line 48 of file Reflection.cs.
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inlinestatic |
Gets the value of a property inside an instance.
| T | The type that the property value should be automatically cast to |
| obj | The instance containing the property |
| name | The name of the property |
| flags | Any optional flags you want to pass in. BindingFlags.Instance is always automatically included |
Definition at line 119 of file Reflection.cs.
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Gets a MethodInfo without internally caching it.
| type | The type containing the method |
| name | The name of the method |
| paramTypes | The parameter types of the method, in order |
| flags | Any optional flags you want to pass in. By default, most flags are automatically included |
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inlinestatic |
| T | : | Attribute |
Definition at line 307 of file Reflection.cs.
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inlinestatic |
Definition at line 327 of file Reflection.cs.
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Gets the type of each parameter passed in.
| parameters | The parameters |
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static |
Gets a PropertyInfo without internally caching it.
| type | The type containing the property |
| name | The name of the property |
| flags | Any optional flags you want to pass in. By default, most flags are automatically included |
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inlinestatic |
Gets the value of a static field in a type.
| T | The type that the retrieved field value should be automatically cast to |
| type | The type containing the field |
| name | The name of the field |
| flags | Any optional flags you want to pass in. BindingFlags.Static is always automatically included |
Definition at line 64 of file Reflection.cs.
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inlinestatic |
Gets the value of a static property in a type.
| T | The type that the property value should be automatically cast to |
| type | The type containing the property |
| name | The name of the property |
| flags | Any optional flags you want to pass in. BindingFlags.Static is always automatically included |
Definition at line 135 of file Reflection.cs.
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inlinestatic |
Definition at line 318 of file Reflection.cs.
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inlinestatic |
Searches the given assembly for types holding the specified attribute.
| T | The type of the attribute |
| assembly | The assembly to search in |
| T | : | Attribute |
Definition at line 297 of file Reflection.cs.
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Checks if a given type has a field.
| type | The type. |
| name | The field name. |
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static |
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static |
Checks whether the given type has a property.
| type | The type. |
| name | The name of the property. |
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inlinestatic |
Invokes a constructor on an object.
| obj | The instance containing the method |
| name | The name of the method |
| flags | Any optional flags you want to pass in. BindingFlags.Instance is always automatically included |
| parameters | The parameters to pass to the method |
Definition at line 229 of file Reflection.cs.
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inlinestatic |
Invokes an instance method and gets its return value.
| T | The type that the return value of the method should be automatically cast to |
| obj | The instance containing the method |
| name | The name of the method |
| flags | Any optional flags you want to pass in. BindingFlags.Instance is always automatically included |
| parameters | The parameters to pass to the method |
Definition at line 191 of file Reflection.cs.
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inlinestatic |
Invokes a static method and gets its return value.
| T | The type that the return value of the method should be automatically cast to |
| type | The type containing the method |
| name | The name of the method |
| flags | Any optional flags you want to pass in. BindingFlags.Static is always automatically included |
| parameters | The parameters to pass to the method |
Definition at line 209 of file Reflection.cs.
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inlinestatic |
Sets the value of a field in an instance.
| obj | The instance containing the field |
| name | The name of the field |
| value | The value to set the field to |
| flags | Any optional flags you want to pass in. BindingFlags.Instance is always automatically included |
Definition at line 79 of file Reflection.cs.
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inlinestatic |
Sets a property in an instance to the specified value.
| obj | The instance containing the property |
| name | The name of the property |
| value | The value to set the property to |
| flags | Any optional flags you want to pass in. BindingFlags.Instance is always automatically included |
Definition at line 150 of file Reflection.cs.
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inlinestatic |
Sets the value of a static field in a type.
| type | The type containing the field |
| name | The name of the field |
| value | The value to set the field to |
| flags | Any optional flags you want to pass in. BindingFlags.Static is always automatically included |
Definition at line 94 of file Reflection.cs.
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inlinestatic |
Sets a static property in a type to the specified value.
| type | The type containing the property |
| name | The name of the property |
| value | The value to set the property to |
| flags | Any optional flags you want to pass in. BindingFlags.Static is always automatically included |
Definition at line 165 of file Reflection.cs.
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Definition at line 14 of file Reflection.cs.
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Definition at line 16 of file Reflection.cs.
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Definition at line 15 of file Reflection.cs.
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Definition at line 17 of file Reflection.cs.
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staticprivate |
Definition at line 22 of file Reflection.cs.
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staticprivate |
Definition at line 19 of file Reflection.cs.
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staticprivate |
Definition at line 21 of file Reflection.cs.
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staticprivate |
Definition at line 20 of file Reflection.cs.


--- layout: default title: "Meshery - a multi-service mesh performance benchmark and playground" description: "Benchmark the performance of your application across different service meshes and compare their overhead. Quickly play with and learn Istio, Linkerd, Envoy, Octarine, App Mesh, Consul Connect." image: /assets/projects/meshery/meshery-logo.gif author: twitter: layer5 lang: en ---


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Making Every Day Mindful
Self confidence is not about making yourself into such a great person
that you trust youtrself to act desirably in any situation. It is about
acknowledging and making peace with your insecurites. There's a Buddhist
premise that says that we can only desire things we do not already have,
and I think in that lies the secret path to all things of the self: confidence,
love, trust, etc... In accepting ourselves the way we are, we are actually
free to be the best version of ourselves. Wow!


Oh No!
There was an error uploading your file. Try again or ask Tyler. He's probably standing nearby.
Return to home


Try to find out how many zeros a given number has at the end.
Input: A positive Int
Output: An Int.
Example:
end_zeros(0) == 1 end_zeros(1) == 0 end_zeros(10) == 1 end_zeros(101) == 0
View solutions
See the task on CheckiO


first_bit_set(i)
Return the index of the least significant bit set in the integer i
. Return -1
if no bits are set.
The input is treated as if represented in two's complement, even if it is not represented that way internally.
first_bit_set(0b00000)
// -1
first_bit_set(0b00001)
// 0
first_bit_set(0b01100)
// 2


--- title: Manim keywords: fastai sidebar: home_sidebar summary: "Recipes for manim" description: "Recipes for manim" nb_path: "nbs/03_modeling/manim.ipynb" ---
from fastcore.basics import patch
from manim import *
import numpy as np
from manim.mobject.geometry import *
from manim.mobject import geometry
@patch
def intersect(self: Circle, obj: Circle, steps=500, tolerance=0.01) -> ArcPolygon:
self_points = [self.point_from_proportion(step) for step in np.linspace(0,1,steps)]
obj_points = [obj.point_from_proportion(step) for step in np.linspace(0,1,steps)]
intersections = []
for a in self_points:
for b in obj_points:
if np.linalg.norm(a-b) < tolerance:
avg = (a+b)/2
intersections.append(avg)
return ArcPolygon(*intersections, radius=1, fill_opacity=0.5, color=PURPLE_A)
%%manim IntersectionDemo
class IntersectionDemo(Scene):
def construct(self):
A = Circle(RED, fill_opacity=0.5, radius=2).shift(LEFT*2)
B = Circle(BLUE, fill_opacity=0.5).shift(RIGHT*2)
# intersection = Cutout(A, B)
self.play(ShowCreation(A), ShowCreation(B))
self.wait(1)
self.play(A.animate.shift(RIGHT), B.animate.shift(LEFT))
intersection2 = Cutout(A,B, color=GREEN)
self.play(ShowCreation(intersection2))
%%manim IntersectionDemo
class IntersectionDemo(Scene):
def construct(self):
A = Circle(RED, fill_opacity=0.5, radius=2).shift(LEFT*2)
B = Circle(BLUE, fill_opacity=0.5).shift(RIGHT*2)
intersection = A.intersect(B)
self.play(ShowCreation(A), ShowCreation(B), ShowCreation(intersection))
self.wait(1)
A2 = Circle(RED, fill_opacity=0.5, radius=2).shift(LEFT*0.5)
B2 = Circle(BLUE, fill_opacity=0.5).shift(RIGHT*0.5)
intersection2 = A2.intersect(B2)
self.play(Transform(A,A2), Transform(B,B2), Transform(intersection, intersection2))
self.wait(1)
%%manim IntersectionDemo
class IntersectionDemo(Scene):
def get_intersections(self, A, B, tolerance=0.01, steps=500):
A_points = [A.point_from_proportion(step) for step in np.linspace(0,1,steps)]
B_points = [B.point_from_proportion(step) for step in np.linspace(0,1,steps)]
intersections = []
for a in A_points:
for b in B_points:
if np.linalg.norm(a-b) < tolerance:
avg = (a+b)/2
intersections.append(avg)
return intersections
def intersect(self, A, B):
intersections = self.get_intersections(A,B)
return ArcPolygon(*intersections, radius=1, fill_opacity=0.5, color=PURPLE_A)
def construct(self):
with register_font('JetBrainsMono-Regular.ttf'):
text = Text('Hello World', font='JetBrainsMono').shift(UP*2)
self.play(Write(text))
A = Circle(RED, fill_opacity=0.5).shift(LEFT*2)
B = Circle(BLUE, fill_opacity=0.5).shift(RIGHT*2)
intersection = self.intersect(A,B)
self.play(ShowCreation(A), ShowCreation(B), ShowCreation(intersection))
self.wait(1)
A2 = Circle(RED, fill_opacity=0.5).shift(LEFT*0.5)
B2 = Circle(BLUE, fill_opacity=0.5).shift(RIGHT*0.5)
intersection2 = self.intersect(A2,B2)
self.play(Transform(A,A2), Transform(B,B2), Transform(intersection, intersection2))
self.wait(1)
%%manim IntersectionDemo -qm -v WARNING
class IntersectionDemo(Scene):
def construct(self):
with register_font('JetBrainsMono-Regular.ttf'):
text = Text('Hello World', font='JetBrainsMono').shift(UP*2)
self.play(Write(text))
A = Circle(RED, fill_opacity=0.5).shift(LEFT*2)
B = Circle(BLUE, fill_opacity=0.5).shift(RIGHT*2)
self.play(ShowCreation(A), ShowCreation(B))
self.play(A.animate.shift(RIGHT*1.5), B.animate.shift(LEFT*1.5))
self.wait(1)
%%manim IntersectionDemo
class IntersectionDemo(Scene):
def construct(self):
s1 = Square().scale(2.5)
s2 = Triangle().shift(DOWN + RIGHT).scale(0.5)
s3 = Square().shift(UP + RIGHT).scale(0.5)
s4 = RegularPolygon(5).shift(DOWN + LEFT).scale(0.5)
s5 = RegularPolygon(6).shift(UP + LEFT).scale(0.5)
c = Cutout(s1, s2, s3, s4, s5, fill_opacity=1, color=BLUE, stroke_color=RED)
self.play(Write(c), run_time=4)
%%manim IntersectionDemo -qm -v WARNING
class IntersectionDemo(Scene):
def construct(self):
s = Vector()
self.play(Write(s))
%%manim IntersectionDemo -qm -v WARNING
class IntersectionDemo(Scene):
def construct(self):
s = Vector()
self.play(Write(s))


Brett Schiff
Contact Info:
<EMAIL_ADDRESS>LinkedIn
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Download Resume as PDF


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Move the Ball
Move your device to move the ball.
Your browser does not support Device Orientation and Motion API. Try this sample with iPhone, iPod or iPad with iOS 4.2+.


Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
Amazon EC2's simple web service interface allows you to obtain and configure capacity with minimal friction.  Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change. Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate themselves from common failure scenarios.
Visit http://aws.amazon.com/ec2/ for more information.


{% set scriptdir = '../common/js/' %} {% set cssdir = '../common/css/' %} {% extends "templates/base.tmpl" %} {% block pagetitle %}Liberty{% endblock %} {% block title %}{% endblock %} {% block header %} {% endblock header %} {% block content %}
What is OpenStack? OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface.
This is an earlier OpenStack release. Use the menu to select a later release if needed.
New features, upgrade and deprecation notes, known issues, and bug fixes
OpenStack Projects Release NotesGetting started with the most commonly used OpenStack services
Installation Guide for openSUSE 13.2 and SUSE Linux Enterprise Server 12 Installation Guide for Red Hat Enterprise Linux 7 and CentOS 7 Installation Guide for Ubuntu 14.04 (LTS)Deploy and manage OpenStack Networking (neutron)
Documentation treated like code, powered by the community - interested?
How To Contribute


We use permissions android.permission.READ_PHONE_STATE, android.permission.READ_CONTACTS. This allows us to read your contacts and display them in dialer.


ProgramDebugInfo(programName: String, dbg: DebugInfo)
Debug info with the program's name
programName - name of the program
programName
dbg - the debugging information
dbg


Ode to the beloved coastal Bullfrog Jerry Shikongo 1951 - 2019
by Carlos KambaekwaHome \ Sports \ Ode to the beloved coastal Bullfrog Jerry Shikongo 1951 - 2019 - New Era Live
Home \ Sports \ Ode to the beloved coastal Bullfrog Jerry Shikongo 1951 - 2019 - New Era Live


Defining "BETTER"
Bytehub as a platform lets you find out technical talents based on their skills and not
instinct.Pick the best talents
by putting skill based assessments to work.This platform give kids and teens a solid
foundation to visualize and create
new products.It provides them the skills to give shape to their vision. Our community
encourages kids and teens to imagine,
create and build.
Student achievement beyond "TEST"
Exposing kids and teens to computer programming helps kindle strong interest in technology
and gives them a
lifelong gift of being able to realize their vision by computer programming (coding).
Awakening the FUTURE
Early exposure to computer science and computer
programming also helps kids in developing
strong critical thinking skills,
which will help them in all areas of life. We believe in giving kids and teens the tools
that will enable them to be
innovators and build products and solutions of the future.
Heading 4
It’s expected that by 2020 there will be 1.4 million computing jobs available but there will
be only 400,000 computer
science students available. It’s a $500 billion opportunity. Even now, around 60% of STEM
(Science, Technology,
Engineering and Math) jobs are Computing (Computer Science / Computer Programming) jobs.
Way ahead with TECHNOLOGY
Technology education expands creativity and
accentuates logical reasoning.These skills are
highly sought after and
ensure satisfying and financially secure future.


{% extends 'base.html' %} {% load static %} {% block extra_head %}
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--- layout: 404 permalink: /404.html --- Page not found You probably shouldn't be here Find something to look at


| DOSE: T+ 0:00 |
1 hit |
oral |
DOM |
(blotter / tab) |
| T+ 1:00 |
0.25 tablets |
oral |
MDMA |
(pill / tablet) |
| T+ 2:30 |
0.5 tablets |
oral |
MDMA |
(pill / tablet) |
My girlfriend and I had recived some blotter from a friend at a party one time and he said that it is like acid but with a much longer duration. I'm here to say though at doses like what we had this is not acid. This was like mescaline, acid and other psycodelics all wrapped into one. We had had the DOM for sometime now and since we hadn't tripped since feb we decided to give it a go.  Seems theres always more to discover this way, so to keep it up beat we got some Ecstacy to go with it.
11:30pm or so: We dropped the two hits, stuck um under our tounges.  It tasted like burning rubber smells. Then after about 30 mins I accidently swallowed mine. So my gf decided to swallow hers to. We sat in my apt for a bit and listened to some music and played some video games. Then after about 1 hour we started to feel it coming. Things started to shimmer a little and at that point we decided to eat a quarter of a roll each. We knew that the DOM would over power the roll but we still wanted to add a little bounce to it.
12am Sunday: We started feeling it come on very very hard. The walls started to melt a little. We are used to this, we've done a lotta acid before, but what happened after this neither of us had experianced before. The walls started to break down. They would swirl into the most beautiful colors of the crazyest kind. There was blue, red, yellow and green. We were also watching cartoons at the time and there was a cat on one of the cartoons. I saw the cat come out of the TV and dance on the air, then on the floor. Crazyest thing is, my gf saw it to.
1am: By this time we were tripping pretty hard, and things were starting to melt into each other and there were colors every where. We decided to turn off the lights and we turned on the black lights and christmas lights. I have a black light poster that opens up in the middle and had lots of colors and lines around the edge. As we were sitting on my couch we were staring at the poster and the poster started to come alive, more then anything that I have seen before. This small poster started to eat up everything, each line became 3 lines and each three lines manifested 3 lines, by the time we had stared at it for 10 mins the whole room was the poster, we were in the middle of a tunnel of colors and still sitting on the couch.  Also during this time we decided to turn off all the lights, big mistake for us.
1:30am: We were listening to a CD called Liquid Acid, its a CD that has a lot of noises that are tweaked out to take your mind to other realms, which is great on acid because of how you can control your mind most of the time. Unfortunatly with this stuff my gf and I couldnt get control. The twaeks of the music became too much and we started to see the noises that were happening, we started to feel the music go through us. We started tipping so hard that we saw thundering colors happening in my apt and we saw smoke billowing from everything. Every molecule could be seen and it was manifested 3 times over.
We could see colors seething out of the walls, we could feel the tinyest of sounds. For me the colors became so real that they turned into clowns. To me it looked like Kisses Psycodelic Circus, unfortunatly my gf doesn't like clowns and when I kept repeating 'the clowns, the clowns there every where' she got very scared and so did I. I also couldn't say Kisses Psycodelic Circus right and when I tried to say it to her. She thought that I was calling her a psycho. And she continued to think that the whole night. That was the moment when things started to get hay wire.
2:30am: We had been tripping in the dark for sometime but had no real concept of time. This stuff makes you feel like you ate an 8 ball of meth and a vial of LSD 25 all at once, our brains were going insane. We decided to take another piece of a roll so I went to get it but when we were going to do it neither of us could focus enough to snort it so we tried our best to eat a half each. After I ate it I started being all monkey like and I was jumping on the furnature, my gf went to the bathroom and when she came back she saw me stretching out. I had my feet on the back of the couch and was stretching using the ceiling to push against, but thats not what she saw.
She saw me on the ceiling. She thought that I was up on the wall, like stuck to it and she saw me moving around it. This is the point where I believe I totally lost her. She couldn't handle the drug any more. She is this tiny women, like 100+- pounds and the most acid shes ever done was 3 hits. She started to cave in. And then I said something like. 'This is psycotic' and she thought that I called her a psycho again. It all went down hill from there but I didn't know that. I didn't know that she was getting mad at me, scared of me.
3am: I don't know what happened at this point. I passed out. I remember being on my room, looking for my gf and her not being anywhere. I remember searching for her in the darkness. But after that I don't remember anything. I had some crazy dreams though. I know that I was a hippie in the 60s at an outdoor fest. I could see myself clear as day, I had a pot belly, torn jeans, bandanas and a jean vest and I was hittin a bong. I don't smoke pot though.
4:30am: I woke up in my apt, in my bed, I had my legs turked under my chest and my head down, the same way that muslems pray. When I woke the blue christmas lights that I have in my apt were on and I felt good. My gf has an apt below me and I figured that she was in there so I went down stairs and I saw her coming up, she looked really worried. We went into my apt and I started to tlk to her, she didnt really say much though, she said that she had gone outside and that she was having a crazy time, but I don't think that she meant good crazy.
When we got to my apt we turned on the lights and there was still color everywhere, everything was liquad, we didn't even have to focus on things any more to make them be liquad, they just were, everything was melting into its self and everything around us. I have turntables and I'm an ok DJ so I put a record on and tried to mix, BIG MISTAKE, the sounds coming out of the sterio were so intense that it hurt. I felt like my ears were gonna bleed.
I was a little sweaty so I decided to hop in the shower. While I was taking a shower everything got even crazyer. Every sound and image became 10 fold by this time, then 10 more, then 10 more. The water falling became this high pitched squeal and the water looked like it was everywhere. I thought that I was flooding the bathroom. Also the hair on my arms looked like it was growing at an insane rate. I felt like I was an animal, a mouse, completly covered in long hairs. I got out of the shower cause I couldn't handle it anymore and dried off and got dressed. My gf was in my room and we decided to turn off the lights and listen to some music. Thats when the most amzing thing ever happened.
5am: I watched complete cities and cizilazations be built. I saw shadows of shadows. I watched life be created from the smallest molicule. I saw aztec ruins be rebuilt, myan cultures come to life roman and greece culters uprise and fall. I floated through space with the wings of life. I layed on my floor and watched the cosmos come to life. We saw stars shoot across the room. We saw life being built. All we could see particals. I don't understand all that I saw and I can't explain it in the best detail but what we saw made me realize how small we truely are. How little I truely know. How great and vast this world is and how many other worlds there are out there. I don't understand it all but knew that someday it will all make sense.
These visuals were unlike any other. It reminded me of all the things that I had heard about peyote and high high doses of mescaline but it wasn't as lucid. I was still in control of me and could think about things but at the same time I could see life. I could walk though the visuals to and when I did they would part. It was the most insane intense visuals ever. I could feel everything. I could feel all of life around me manifesting. I could feel everything that is except that fact that my gf was getting more and more distant from me. It seems that in that time that I had been asleep she had lost our path. And I was having a hell of a time trying to find her.
7am: At this point we had be tripping for about 8 hours and it was just as strong as it had been before and I could feel it getting stronger. Before I passed out it had been a very happy trip, very colorful and enlightning. After I woke it was the same but as the day went on it kept getting more and more intense. It got meaner and meaner and meaner. It was like the anger from my gf and I was being manifested its and as we grew further and further apart the drug got meaner and meaner. The dark side of it knew that we were now vulnerable and it started to attack. The things that we had seen before like the swirling vortexes had now become even more insane, the drug was getting stronger and stronger, after 9 hours it was still going strong and getting stronger.
9am: The walls began to melt to black. There was still color but it was so intense that our eyes could barely handle it, it burned our eyes to stare at anything to long. The smallest of things like writing on a CD became everything that I saw.  It was insane. By this time it was like I was walking in water every step that I made. I had to wade through the colors. Around this time my gf had also been completely lost and I was getting there too. I've done alot of acid and research chems before so I knew that they had a long duration but most things get lesser in time this just kept getting stronger though. We saw basically a circus in the carpets and the walls, there were so many colors and they were so strong that it was unbearible.
Noon: We tried to go to the park so that we could get out of our apts that had become little prisons because of all the evil that had been manifested in there. I couldn't make it though. I was tripping far too hard. We got about halfway down the block then I broke. I turned back. I wish that I never had. My gf was very dissapointed in me.
5pm: Still trippin just as hard as before. It never stopped. The colors and presences of images, full images that I could touch and grab were still there. I tried to move my gf from one room to another so that her eyes wouldn't get to burnt from staring at things but she didn't understand why I was doing it so she started to get mad at me and I wasn't in the kinda mind set where I could explain it to her. I was trying to do my best to protect her, but I was acting pushy and my pushy became mean to her. Everything was basically turned inside out and upside down for us. When I got too pushy she locked her self in her apt and wouldn't let me in.
8 pm: My gf finally let me in about this time. Turns out that she had taken a shower. And we were both still tripping just as hard as 5 hours ago. My gf and I talkd on the couch and I could tell that she was hurting. She was mad at me. She was in pain. I tried to find out what was wrong but I couldn't figure it out. I tried to find out where I had lost her but I realized that this is such a snakes and ladders drug that she could be any where on the cosmos spectrum.
 She watched her self die, it was really fucked. We laid in her bed for awhile and then we decided to try the park again since it was dark now. The trip wasn't going as hard as before but we were still tripping balls. I would compare it to about 60 hits of acid at this point rather then the 100 that it had been before.
9 pm: Made it to the park. Stared at the sky and I saw total mirror images. The most beautiful ever. I saw a shooting star become 1000 shooting stars. I saw orbs and pyramids or colors all over the night sky. I also saw writing that looked like hyroglifics all over the sky. My gf and I walkd further through the park, which is really three parks connected to one another, and we walked down a trail of complete darkness. We could see the same images that we had seen in the sky in front of us clear as day. When I lightning bug would glow we could see it streak all the way across the trail.
We came to a bridge and we sat there for sometime talking. We looked at the beautiful colors of the night sky and we also saw light in the sky bright as day, the sky would become so bright sometime that it felt like it was day. We talked for about an hour or so on the bridge about alot of things. Then we proceded further into the park. When we came across the bridge the park completely opens up into what could be compared to a field. The park looked like it was covered in a haze of fog and smoke all over rolling down the hills. Only thing was that there was no fog or smoke. We walked further into the park and then stopped at a frisbee golf hole to rest. We stayed there for sometime and then decided to head back.
2:30am Monday: We walked back to our apts and when we went inside we decided to try and sit down and watch a movie. We watched family guy and talkd for a long time. I was still trying to find her but I couldn't. I didn't know where she was anymore. I didn't even really know where I was.
4am: We decided to lay in bed and we turned on some music to try and relax. Everything was still swirling though. Not as much as 9 hours ago but still hella hard. We laid in bed and we kept getting further and further away from one another. Everytime I would try and find her she would run further away. I didn't understand it then but I do now. She thought that I was going to hurt her because of earler when I had tried to take control. We layed in bed and listened to music and tried to figure shit out. Problem was by this time we were so sleep deprivated and tripped out that we couldn't really make sense of anything, especially each other. This happened from when we laid in be till about 3 in the afternoon.
3pm: Finally my eyes start to get heavey and I'm finally starting to fall asleep.
4pm: Fall asleep.
5:15pm: My friend comes to my gfs apt and wakes me up. By this time my gf had already headed to work since she worked at 5. I was still tripping when I got woken up. I was still tripping just as hard as when I had gone to sleep and I had to go to work now.
5:30pm: Get to work still tripping. I had also been crying my eyes out the whole way so I looked like a wreck. Sat down in my seat (I work at an after hours secritary kinda place) and tried to look normal. I only work with one other guy and hes seen me pretty wrecked before so I got away with it. He left at 7 and I was all alone. Turned off the lights in the office cause they stung my eyes so bad that I could barely keep them open.
12:01am Tuesday: Not tripping as hard anymore. Mostly sleep deprevation. Headed home to see my gf and when I got there she was already outside. We went for a walk and she explained alot of the things that had happened the night b4 and I explained to her what had happened as well. We talkd for some time and then we headed home.
2am: My gf made me dinner and we sat and watched to movie together. Talked more about how the night had unfolded and more about what we were feeling. We realized how lost we had been and how much we truely mean to one another. Then we went to bed about 3 am.
4am: Finally got to sleep.
6am: Got woken up by a phone call. Turned phone to silent :)
3pm: Woke up to get ready for work. My body ached.  But my heart was whole again. I felt normal again. I was feeling ok. My gf and I had had a long talk b4 we went to bed and that made things much better. Things felt right again, I felt whole again.
 It was easily and most defanitly the craziest most psycotic trip I had ever had. I saw the beauty of all the world but I also saw complete distruction. I saw all the beauty that my gf has in her heart and her soul but I also saw all the pain and anger that she holds to. I saw everything. I was completly turned inside out and upside down.
I watched life be created from the smallest molicule and all the teachings that I've ever learned became truth. I saw aztec ruins be rebuilt, myan cultures come to life roman and greece culters uprise and fall, I floated through space with the wings of life. I questioned everything, held on to nothing, forgot all that was and learned all that really is. I saw so much and have so much knowledge now that I don't even know how to explain some of it.
I've come to realize that all those quotes that I know like 'to realize everything, you most realize that you are nothing' are true. It was a hell of a ride though with so many twists and turns that I lost my gf many times over as well as my self and a piece of us died over the past couple days but we are rebuilding now and will be even stronger in the end because of this. Because we saw complete bottom, by seeing it we realized that thats not us. We will never be at the bottom, we will never allow it. I realized how lost I am with out her and her with I. I realized that I truely truely love her.
We completly lost one another over the last couple days but by totally losing one another and ourselves we discovered how much we love one another and how strong our bond is. There are things that my gf was not supposed to experiance yet, and things that I wasn't supposed to either. I'm glad that we did because there was so much that we both learned. There's so much that this drug can share, you just have to be very very careful not to let the wrong things and the evil things take to much hold.
Thank you for reading this. I hope that it was informative. I wish Peace, Love, Unity and Respect to all of you. And again thank you.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


#!/usr/bin/env python
"""Analysis of inequality.
This package provide an easy way to realize a quantitative analysis of
grouped, also make easy work with stratified data, in this module you can
find statistics and grouped indicators to this task.
Todo
----
- Rethinking this module as Class.
- https://en.wikipedia.org/wiki/Income_inequality_metrics
"""
import numpy as np
import pandas as pd
from .statistics import mean
from . import utils
[docs]def concentration(income, weights=None, data=None, sort=True):
"""This function calculate the concentration index, according to the
notation used in [Jenkins1988]_ you can calculate the:
C_x = 2 / x · cov(x, F_x)
if x = g(x) then C_x becomes C_y
when there are taxes:
y = g(x) = x - t(x)
Parameters
----------
income : array-like
weights : array-like
data : pandas.DataFrame
sort : bool
Returns
-------
concentration : array-like
References
----------
Jenkins, S. (1988). Calculating income distribution indices
from micro-data. National Tax Journal. http://doi.org/10.2307/41788716
"""
# TODO complete docstring
# check if DataFrame is passed, if yes then extract variables else make a copy
income, weights = utils.extract_values(data, income, weights)
if weights is None:
weights = utils.not_empty_weights(weights, as_of=income)
# if sort is true then sort the variables.
if sort:
income, weights = utils._sort_values(income, weights)
# main calc
f_x = utils.normalize(weights)
F_x = f_x.cumsum()
mu = np.sum(income * f_x)
cov = np.cov(income, F_x, rowvar=False, aweights=f_x)[0,1]
return 2 * cov / mu
[docs]def lorenz(income, weights=None, data=None):
"""In economics, the Lorenz curve is a graphical representation of the
distribution of income or of wealth. It was developed by Max O. Lorenz in
1905 for representing grouped of the wealth distribution. This function
compute the lorenz curve and returns a DF with two columns of axis x and y.
Parameters
----------
data : pandas.DataFrame
A pandas.DataFrame that contains data.
income : str or 1d-array, optional
Population or wights, if a DataFrame is passed then `income` should be a
name of the column of DataFrame, else can pass a pandas.Series or array.
weights : str or 1d-array
Income, monetary variable, if a DataFrame is passed then `y`is a name
of the series on this DataFrame, however, you can pass a pd.Series or
np.array.
Returns
-------
lorenz : pandas.Dataframe
Lorenz distribution in a Dataframe with two columns, labeled x and y,
that corresponds to plots axis.
References
----------
Lorenz curve. (2017, February 11). In Wikipedia, The Free Encyclopedia.
Retrieved 14:34, May 15, 2017, from
https://en.wikipedia.org/w/index.php?title=Lorenz_curve&oldid=764853675
"""
if data is not None:
income, weights = utils.extract_values(data, income, weights)
total_income = income * weights
idx_sort = np.argsort(income)
weights = weights[idx_sort].cumsum() / weights.sum()
weights = weights.reshape(len(weights), 1)
total_income = total_income[idx_sort].cumsum() / total_income.sum()
total_income = total_income.reshape(len(total_income), 1)
res = pd.DataFrame(np.c_[weights, total_income], columns=['Equality', 'Income'],
index=weights)
res.index.name = 'x'
return res
[docs]def gini(income, weights=None, data=None, sort=True):
"""The Gini coefficient (sometimes expressed as a Gini ratio or a
normalized Gini index) is a measure of statistical dispersion intended to
represent the income or wealth distribution of a nation's residents, and is
the most commonly used measure of grouped. It was developed by Corrado
Gini.
The Gini coefficient measures the grouped among values of a frequency
distribution (for example, levels of income). A Gini coefficient of zero
expresses perfect equality, where all values are the same (for example,
where everyone has the same income). A Gini coefficient of 1 (or 100%)
expresses maximal grouped among values (e.g., for a large number of
people, where only one person has all the income or consumption, and all
others have none, the Gini coefficient will be very nearly one).
Parameters
---------
data : pandas.DataFrame
DataFrame that contains the data.
income : str or np.array, optional
Name of the monetary variable `x` in` df`
weights : str or np.array, optional
Name of the series containing the weights `x` in` df`
sorted : bool, optional
If the DataFrame is previously ordered by the variable `x`, it's must
pass True, but False by default.
Returns
-------
gini : float
Gini Index Value.
Notes
-----
The calculation is done following (discrete probability distribution):
G = 1 - [∑_i^n f(y_i)·(S_{i-1} + S_i)]
where:
- y_i = Income
- S_i = ∑_{j=1}^i y_i · f(y_i)
Reference
---------
- Gini coefficient. (2017, May 8). In Wikipedia, The Free Encyclopedia.
Retrieved 14:30, May 15, 2017, from
https://en.wikipedia.org/w/index.php?title=Gini_coefficient&oldid=779424616
- Jenkins, S. (1988). Calculating income distribution indices
from micro-data. National Tax Journal. http://doi.org/10.2307/41788716
TODO
----
- Implement statistical deviation calculation, VAR (GINI)
"""
return concentration(data=data, income=income, weights=weights, sort=sort)
[docs]def atkinson(income, weights=None, data=None, e=0.5):
"""More precisely labelled a family of income grouped measures, the
theoretical range of Atkinson values is 0 to 1, with 0 being a state of
equal distribution.
An intuitive interpretation of this index is possible: Atkinson values can
be used to calculate the proportion of total income that would be required
to achieve an equal level of social welfare as at present if incomes were
perfectly distributed.
For example, an Atkinson index value of 0.20 suggests
that we could achieve the same level of social welfare with only
1 – 0.20 = 80% of income. The theoretical range of Atkinson values is 0 to 1,
with 0 being a state of equal distribution.
Parameters
---------
income : array or str
If `data` is none `income` must be an 1D-array, when `data` is a
pd.DataFrame, you must pass the name of income variable as string.
weights : array or str, optional
If `data` is none `weights` must be an 1D-array, when `data` is a
pd.DataFrame, you must pass the name of weights variable as string.
e : int, optional
Epsilon parameter interpreted by atkinson index as grouped adversion,
must be between 0 and 1.
data : pd.DataFrame, optional
data is a pd.DataFrame that contains the variables.
Returns
-------
atkinson : float
Reference
---------
Atkinson index. (2017, March 12). In Wikipedia, The Free Encyclopedia.
Retrieved 14:35, May 15, 2017, from
https://en.wikipedia.org/w/index.php?title=Atkinson_index&oldid=769991852
TODO
----
- Implement: CALCULATING INCOME DISTRIBUTION INDICES FROM MICRO-DATA
http://www.jstor.org/stable/41788716
- The results has difference with stata, maybe have a bug.
"""
if (income is None) and (data is None):
raise ValueError('Must pass at least one of both `income` or `df`')
income, weights = utils.extract_values(data, income, weights)
weights = utils.not_empty_weights(weights, income)
# not-null condition
income, weights = utils.not_null_condition(income, weights)
# not-empty condition
if len(income) == 0:
return 0
# auxiliar variables: mean and distribution
mu = mean(variable=income, weights=weights)
f_i = weights / sum(weights) # density function
# main calc
if e == 1:
atkinson = 1 - np.power(np.e, np.sum(f_i * np.log(income) - np.log(mu)))
elif (0 <= e) or (e < 1):
atkinson = 1 - np.power(np.sum(f_i * np.power(income / mu, 1 - e)),
1 / (1 - e))
else:
assert (e < 0) or (e > 1), "Not valid e value, 0 ≤ e ≤ 1"
atkinson = None
return atkinson
[docs]def kakwani(tax, income_pre_tax, weights=None, data=None):
"""The Kakwani (1977) index of tax progressivity is defined as twice the
area between the concentration curves for taxes and pre-tax income,
or equivalently, the concentration index for t(x) minus the Gini index for
x, i.e.
K = C(t) - G(x)
= (2/t) cov [t(x), F(x)] - (2/x) cov [x, F(x)].
Parameters
----------
data : pandas.DataFrame
This variable is a DataFrame that contains all data required in
columns.
tax_variable : array-like or str
This variable represent tax payment of person, if pass array-like
then data must be None, else you pass str-name column in `data`.
income_pre_tax : array-like or str
This variable represent income of person, if pass array-like
then data must be None, else you pass str-name column in `data`.
weights : array-like or str
This variable represent weights of each person, if pass array-like
then data must be None, else you pass str-name column in `data`.
Returns
-------
kakwani : float
References
----------
Jenkins, S. (1988). Calculating income distribution indices from micro-data.
National Tax Journal. http://doi.org/10.2307/41788716
"""
# main calc
c_t = concentration(data=data, income=tax, weights=weights, sort=True)
g_y = concentration(data=data, income=income_pre_tax, weights=weights,
sort=True)
return c_t - g_y
[docs]def reynolds_smolensky(income_pre_tax, income_post_tax, weights=None,
data=None):
"""The Reynolds-Smolensky (1977) index of the redistributive effect of
taxes, which can also be interpreted as an index of progressivity
(Lambert 1985), is defined as:
L = Gx - Gy
= [2/x]cov[x,F(x)] - [2/ybar] cov [y, F(y)].
Parameters
----------
data : pandas.DataFrame
This variable is a DataFrame that contains all data required in it's
columns.
income_pre_tax : array-like or str
This variable represent tax payment of person, if pass array-like
then data must be None, else you pass str-name column in `data`.
income_post_tax : array-like or str
This variable represent income of person, if pass array-like
then data must be None, else you pass str-name column in `data`.
weights : array-like or str
This variable represent weights of each person, if pass array-like
then data must be None, else you pass str-name column in `data`.
Returns
-------
reynolds_smolensky : float
References
----------
Jenkins, S. (1988). Calculating income distribution indices from micro-data.
National Tax Journal. http://doi.org/10.2307/41788716
"""
g_y = concentration(data=data, income=income_post_tax, weights=weights)
g_x = concentration(data=data, income=income_pre_tax, weights=weights)
return g_x - g_y
[docs]def theil(income, weights=None, data=None):
"""The Theil index is a statistic primarily used to measure economic
grouped and other economic phenomena. It is a special case of the
generalized entropy index. It can be viewed as a measure of redundancy,
lack of diversity, isolation, segregation, grouped, non-randomness, and
compressibility. It was proposed by econometrician Henri Theil.
Parameters
----------
data : pandas.DataFrame
This variable is a DataFrame that contains all data required in it's
columns.
income : array-like or str
This variable represent tax payment of person, if pass array-like
then data must be None, else you pass str-name column in `data`.
weights : array-like or str
This variable represent weights of each person, if pass array-like
then data must be None, else you pass str-name column in `data`.
Returns
-------
theil : float
References
----------
Theil index. (2016, December 17). In Wikipedia, The Free Encyclopedia.
Retrieved 14:17, May 15, 2017, from
https://en.wikipedia.org/w/index.php?title=Theil_index&oldid=755407818
"""
if data is not None:
income, weights = utils.extract_values(data, income, weights)
else:
income = income.copy()
weights = weights.copy()
income, weights = utils.not_null_condition(income, weights)
# variables needed
mu = mean(variable=income, weights=weights)
f_i = utils.normalize(weights)
# main calc
theil = np.sum((f_i * income / mu) * np.log(income / mu))
return theil
[docs]def avg_tax_rate(total_tax, total_base, weights=None, data=None):
"""This function compute the average tax rate given a base income and a
total tax.
Parameters
----------
total_base : str or numpy.array
total_tax : str or numpy.array
data : pd.DataFrame
Returns
-------
avg_tax_rate : float or pd.Series
Is the ratio between mean the tax income and base of income.
Reference
---------
Panel de declarantes de IRPF 1999-2007: Metodología, estructura y variables.
(2011). Panel de declarantes de IRPF 1999-2007:
Metodología, estructura y variables. Documentos.
"""
if isinstance(total_base, (np.ndarray)):
n_cols = total_base.shape[1]
elif isinstance(total_base, (list)):
n_cols = len(total_base)
elif isinstance(total_base, (str)):
n_cols = 1
else:
n_cols = total_base.shape[1]
numerator = mean(data=data, variable=total_tax, weights=weights)
denominator = mean(data=data, variable=total_base, weights=weights)
# main calc
res = numerator / denominator
if data is not None:
base_name = total_base
tax_name = total_tax
else:
base_name = ['base' % i for i in range(n_cols)]
tax_name = ['tax_%s' % i for i in range(n_cols)]
names = ['_'.join([t, b]) for t, b in zip(tax_name, base_name)]
res = pd.Series(res, index=names)
return res


Due to its lush tropical trees, shady parks and beautiful lakes, Yangon has earned the name of "The Garden City of the East".
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Last updated March 2013.


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Results 1 - 15 of 313 . AAA Insurance- Talk To An Agent In Your Area . Bravos William H Insurance Service . 520 Olive St Suite A, Marysville, CA 95901 .
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Bravo Reimbursement Specialists provides coding, billing, and patient account . Pay Your BillClick here to pay your bill if you have already entered insurance .
https://www.bravo-brs.com/
Sky Sport and Spa - Beverly Hills - Beverly Hills, CA
. so glad he did! Their cooperation with insurance plans makes working out and chiropractic care extremely
. Bottom line: This is no longer the Skysport of the Bravo show "Work Out. . and I had to call back and fight with them over the bill.
http://www.yelp.com/biz/sky-sport-and-spa-beverly-hills
Randal gaines farmers insurance in Yuba City, CA - Yellowpages.com
2 listings of Life Insurance in Yuba City on YP.com. . 438 Colusa Ave Ste C, Yuba City, CA 95991<PHONE_NUMBER> . Bravos William H Insurance Service .
http://www.yellowpages.com/yuba-city-ca/randal-gaines-farmers-insurance
Medical-Vocational Allowance for Herniated Disc
Yuba City Insurance | Find Insurance in Yuba City, CA
. City, CA. Find businesses and services in Yuba City, CA with MyYP.com. . . Bravos William H Insurance Services520 Olive Marysville 95901<PHONE_NUMBER> .
http://www.myyp.com/Yuba-City,CA/Insurance
Marysville Cheap Auto Insurance ? Quotes, Agents, & More
Bill Bravos Insurance. Phone:<PHONE_NUMBER> 520 Olive St Ste A, Marysville, CA 95901. Card-bottom-large. Card-top-large .
http://www.cheapautoinsurance.org/agents/california/marysville
Marysville, CA Yellow Pages
Bill Bravos Insurance Services 520 Olive St Marysville, CA 95901. Phone :<PHONE_NUMBER>. Product & Services: Insurance Insurance Carriers Miscellaneous .
http://www.theusaexplorer.com/California/Marysville.html
Appealing a Denial of Benefits
Bill Pay - Bravo!
Bravo Reimbursement Specialists . Bill Pay | Pay Your Bill Quickly and Securely! If you are attempting to pay an eMedical Offices bill, please click here.
https://www.bravo-brs.com/pay.php
San Diego News, Local, California and National News ...
The Michigan House approved the first of two right-to-work bills Tuesday that would weaken union power in the . CORRECTIONS · NEW INSURANCE FEE INCLUDED IN HEALTH OVERHAUL LAW . Q&A: Chuy Bravo's birthday bash .
https://www.utsandiego.com/


'Hockey Night in Canada' host Ron MacLean receives honorary degree in Edmonton
by CTV News Edmonton, StaffEDMONTON -- Days after addressing the firing of his long-time co-host Don Cherry on-air, "Coach's Corner" broadcaster Ron MacLean accepted an honorary degree from the University of Alberta.
MacLean was presented with an honorary doctor of laws degree at a 3 p.m. convocation ceremony that will also see degrees bestowed to language rights advocate Cladette Tardif and poet Alice Major.
MacLean also addressed hundreds of graduates from several different faculties following the ceremony.
It came days after MacLean spoke to hockey fans across the country on "Hockey Night in Canada's" Coach's Corner Saturday night, addressing Cherry's firing by Sportsnet and the controversial comments that prompted it.
The long-running segment was "no more," MacLean said in a five-minute address.
"You know Don—defiant. There were steps that needed to be taken because of what had been said by Don. And he didn't want to do those steps," MacLean said. "So he made his choice and I made mine."
The Saturday before, Cherry, 85 years old, seemingly accused new immigrants to Canada of not wearing Remembrance Day poppies, saying "You people that come here…you love our way of life, you love our milk and honey. At least you could pay a couple of bucks for a poppy or something like that."
Sportsnet has said it will take the first intermission segment in a new direction following the controversy.


The IRT is specifically tasked with intaking and addressing any
reports of violation of the Code of Conduct or other member
behavior issues. At any time, you can:
Talk to an IRT staff member on Discord.
File a report using the online form.
File a report at<EMAIL_ADDRESS>More information about what to expect when reporting is available here.
To get help with other issues or concerns, contact Safety.


BAT out of Hell
The sirens are screaming and the fires are howling way down in the (bitcoin twitter) valley tonight. An enlightening conversation between Francis Pouliot and Brendan Eich (CEO of Brave.com) has revealed why brave uses its own token (BAT — Basic Attention Token) rather than Bitcoin, and the rationale is frankly insane. Brave is an unnecessary product for users (ublock origin blocks all ads) and simple ‘pay to read’ tools like satoshipay (but using bitcoin) are far superior.
Bitcoin Mining Trends
Mining companies earn bitcoin from both the block reward and transaction fees. Currently the block reward is 12.5 BTC and the transaction fees per block = ~0.1 BTC. Though there have been times when the block fees almost exceeded block the reward, it has historically been far lower.
Block Reward Per Block [Source]
Transaction Fees per Block [Source]
Going forwards, the issuance of bitcoin decreases (next halving of the block reward is in ~ 1 Year).
Get The Fiat Out
Going Abroad Going abroad temporarily to a place with a different currency can be disorientating. You may struggle judging prices and clumsily convert back to your ‘home’ currency.
Living abroad you adapt and start pricing things in the local currency. You stop thinking in terms of your ‘home’ currency.
It’s time to make the move to the internet.
It’s time to think in bitcoin.
Internalise If you always use GPS you will never learn the route.
Lightning Network Intro
What is lightning? Instant & cheap bitcoin transactions.
How can I try lightning? An easy way to try the lightning network. Just visit tippin.me and sign up. Use a bitcoin lightning wallet such as lightning-wallet on android. On iOS there is thin wallet but that is not yet opensource. Follow the instructions in the app you download to create a payment channel and send some satoshis to your tippin.me account.

If any other person knows this private key, they could spend the UTXO.
For this reason, you must keep your private keys secure, and secret.
Scams & Frauds Scammers have been attempting to take bitcoin from uneducated users for years. One way to do this is to give an uneducated user a bitcoin wallet for which the scammer knows the private key.
Slice The Pie
What even is orange pie?

“The carpenter can’t run out of inches The stadium can’t run out of points The airline can’t run out of FF miles And the USA can’t run out of dollar” - Stephanie Kelton
Understanding There is a common belief that the USA can just print as much money as it wants. Ignoring the complex nature in which money is actually created (debt, treasury notes etc) this is true, the USA can print as many dollars as it likes.
Surplus Clean Energy Falacy
I recently had a long discussion on twitter with StopAndDecrypt about surplus clean energy bitcoin mining. This post explains why surplus clean energy bitcoin mining is a fallacy. Having written this out I have a feeling that S&D and I agree more than we disagree on this.
The post that started the discussion was the following statement;
“If we can create an immutable ledger WITHOUT burning so much energy, why wouldn’t we seek it out?


All About Patrick McNamee
Bio
My name is Patrick McNamee and I am 25 years old. I have lived in Washington State my entire life. My normal everyday hobbies are playing video games with friends and taking my dog Orion for walks/playing fetch with him.
Work History
I worked in construction installing data lines and server rooms in commerical buildings. I did this while i decided what career i wanted to persue.
Education
Highschool followed by general classes at Green River Community college. Ended up stopping doing general classes as i didn't know what i wanted to do or persue and was wasting money at that point.
My Goals
To get a job in the tech industry


Vendor Name:
RICOH CANADA
Reference Number:
8026723
Contract Date:
2012-02-04
Description of Work:
RENTAL OF MACHINERY, OFFICE FURNITURE AND FIXTURES
Contract Period:
2012-02-04 to 2013-30-06
Delivery Date:
2013-30-06
Original Contract Value:
$38,970.00
Current Contract Value:
$41,199.00
Comments:
This contract is a call-up against a Public Works and Government Services Canada procurement tool. It includes one or more amendments (additional unforeseen requirement).
Former Public Servant in Receipt of a Pension under the Public Service Superannuation Act:
No


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execute( Symfony\Component\Console\Input\InputInterface $input , Symfony\Component\Console\Output\OutputInterface $output )
Executes the current command.
This method is not abstract because you can use this class
as a concrete class. In this case, instead of defining the
execute() method, you set the code to execute by passing
a Closure to the setCode() method.
seesetCode() |
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protected
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#
interact( Symfony\Component\Console\Input\InputInterface $input , Symfony\Component\Console\Output\OutputInterface $output )
Interacts with the user.
This method is executed before the InputDefinition is validated.
This means that this is the only place where the command can
interactively ask for values of missing required arguments.
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protected
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#
initialize( Symfony\Component\Console\Input\InputInterface $input , Symfony\Component\Console\Output\OutputInterface $output )
Initializes the command just after the input has been validated.
This is mainly useful when a lot of commands extends one main command
where some things need to be initialized based on the input arguments and options.
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mergeApplicationDefinition( bool $mergeArgs = true )
Merges the application definition with the command definition.
This method is not part of public API and should not be used directly.
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setDefinition( array|\InputDefinition $definition )
Sets an array of argument and option instances.
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Gets the InputDefinition to be used to create representations of this Command.
Can be overridden to provide the original command representation when it would otherwise
be changed by merging with the application InputDefinition.
This method is not part of public API and should not be used directly.
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addArgument( string $name , int $mode = NULL, string $description = '', mixed $default = NULL )
Adds an argument.
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Sets the process title of the command.
This feature should be used only when creating a long process command,
like a daemon.
PHP 5.5+ or the proctitle PECL library is required
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Returns the processed help for the command replacing the %command.name% and
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| Cache<KEY,VALUE> |
Cache
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| ArrayUtils | |
| BaseJsonParser |
Base JSON parser.
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| FastStringUtils | |
| IO | |
| JsonFastParser |
This works in index overlay mode or chop mode.
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| JsonParserCharArray |
Converts an input JSON String into Java objects works with String or char array
as input.
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| JsonParserLax | |
| JsonParserUsingCharacterSource |
Converts an input JSON String into Java objects works with String or char array
as input.
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| LazyMap |
This maps only builds once you ask for a key for the first time.
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This class is important to the performance of the parser.
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This holds a mapping from value key to value value to maximize laziness.
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| ReaderCharacterSource | |
| SimpleCache<K,V> | |
| ValueContainer | |
| ValueList | |
| ValueMapImpl |
This map is for object serialization mainly.
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| Type |
| Exception | Description |
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| Exceptions.JsonInternalException |


WATERLAND (1992) A film review by Vince Deehan Copyright 1996 Vince Deehan
Directed by Stephen Gyllenhaal Written by Peter Prince. Based on the novel by Graham Swift Cast: Jeremy Irons, Sinead Cusack, Ethan Hawke, Grant Warnock, Lena Headey, David Morrissey and John Heard.
WATERLAND is an unusual and original film set in both the US and the UK concerning the life of Tom Crick (Jeremy Irons), an English schoolteacher of History now settled in Pittsburgh, telling his American pupils of his tragic past in England. Irons' character has been settled in the US for about twenty years, although settled is perhaps not a word that could be used to describe his life. His wife Mary (played by Irons' real-life wife Sinead Cusack) tells Tom that she anticipates the prospect of having a child soon. This worries Tom, who knows that Mary is incapable of having children, and he immediately becomes concerned that he may be "losing" his wife. Losing her to the pain and trauma of her childhood.
Tom teaches at a high school in Pittsburgh and we see Tom teaching History to a class of teenagers in 1974. The kids don't seem particularly interested and when one of the pupils, Matthew Price (Ethan Hawke), articulates this disinterest directly to Tom, the teacher finds himself resorting to telling his own personal history in a wildly misguided attempt to capture the pupils attention. Tom tells the class of his teenage years in East Anglia on the flat east coast of England. His story concerns his relationship with his sweetheart Mary (now played by Lena Headey). The kids are partly amused and partly shocked when Tom describes how he and Mary had sex on trains when they were only sixteen. One pupil voices her concern and tells him how she is now sixteen and states she is not sexually active herself and clearly shows her discomfort. Although this partly puts Tom off his stride, he continues to regale the pupils with his stories.
Tom tells them of how his older and slightly retarded brother Dick (David Morrissey) also took a shine to Mary. When young Mary tells Tom that she has seen Dick following her around on his motorbike, Tom unwisely suggests that Mary should talk to his brother so as to teach him what girls are like and so that this would enable him to be able to get a girlfriend of his own. Tom unwittingly sows the seeds here for an imminent tragedy that will have a profound effect on his life forever.
WATERLAND's strength lies in the great script, excellent cinematography of the flat and slightly eerie flatlands of Anglia by Robert Elswit, and some outstanding acting. Chief acting honours go to Jeremy Irons and Lena Headey. Irons plays a man who is not exactly bursting with charisma but Irons makes you interested in this rather tragic character. Lena Headey who plays the young Mary, as a sixteen year old, is outstanding in a very demanding role that would be beyond the reach of many young actors. Unfortunately Grant Warnock as the young Tom, while good, is no match for Headey's skills and rather lets their scenes down somewhat.  Sinead Cusack as the adult Mary is badly letdown by the false teeth she is made to wear in the film, that frankly make her look preposterous and detracted my attention from her good performance.
WATERLAND is a quiet film which tells an intriguing story and tells it with great style.
Review written on Sun 3rd March 1996 by Vince Deehan<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% extends "layouts/base.html" %} {% block title %} My Plans {% endblock %} {% block stylesheets %} {% endblock stylesheets %} {% block content %}
Home My Plans
Per Month
1000 notifications
1 app allowed
$2 one-time every 1000 notifications additional app $10 per month
5000 notifications
3 apps allowed
$8 one-time every 5000 notifications additional app $10 per month


Use Ontologenius Query
Ontologenius_query can be used in a multi-ontological use or for a single ontology use.
Single ontology
Launch ontologenius first with one of the available launch files or with an overloaded launch file.
roslaunch ontologenius ontologenius.launch
Then run the ontologenius_query executable for single ontology.
rosrun ontologenius_query ontologenius_query
A new rosservice will apear under the name: "ontologenius_query/fullQuery"
In the single mode, the parameter 'ns' is unused. Just write your query in the parameter 'query'.
Multi ontology
Launch ontologenius first with one of the available launch files or with an overloaded launch file using ontologenius_multi.
roslaunch ontologenius ontologenius_multi.launch
Then run the ontologenius_query executable for multi-ontology.
rosrun ontologenius_query ontologenius_query_multi
A new rosservice will apear under the name: "ontologenius_query/fullQuery"
In the multi mode, put the name of the targetted ontology in the parameter 'ns' and write your query in the parameter 'query'.
Query Examples
Bedroom isA ?type
results:
-
names: [type]
values: [Room]
Give to class of Bedroom.
?table isA Table
results:
-
names: [table]
values: [Table2]
-
names: [table]
values: [Table1]
Give all the tables.
Table1 isIn ?room
results:
-
names: [room]
values: [Bedroom]
Give all the individuals for which the individual 'Table1' is in it.
?object isA Table, ?object isIn ?room
results:
-
names: [object, room]
values: [Table2, Bedroom]
-
names: [object, room]
values: [Table1, Bedroom]
Give all the individuals that are tables and the room in which they are.
?color isA Color, ?color hasLabel ?label
results:
-
names: [color, label]
values: [Red, red]
-
names: [color, label]
values: [Blue, blue]
Give all the colors and their labels.


A class which adds optimization-specific utilities to simplify routines and synchronization between the control field from algorithms and analysis models.
More...
|
| None | __init__ (self, MasterControl master_control, ResponseFunction response) |
| |
| MasterControl | GetMasterControl (self) |
| |
| def | Initialize (self) |
| | Initializes the response routine. More...
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| |
| def | Check (self) |
| |
| def | Finalize (self) |
| |
| ResponseFunction | GetReponse (self) |
| |
| float | CalculateValue (self, KratosOA.CollectiveExpression control_field) |
| | Calculates the value of the response. More...
|
| |
| KratosOA.CollectiveExpression | CalculateGradient (self) |
| | Returns Collective expression containing all the control space gradients for all control variable types (fields). More...
|
| |
| 'dict[SupportedSensitivityFieldVariableTypes, KratosOA.CollectiveExpression]' | GetRequiredPhysicalGradients (self) |
| | Returns required physical gradients by this response. More...
|
| |
A class which adds optimization-specific utilities to simplify routines and synchronization between the control field from algorithms and analysis models.
◆ __init__()
| None responses.response_routine.ResponseRoutine.__init__ |
( |
|
self, |
|
|
MasterControl |
master_control, |
|
|
ResponseFunction |
response |
|
) |
| |
◆ CalculateGradient()
| KratosOA.CollectiveExpression responses.response_routine.ResponseRoutine.CalculateGradient |
( |
|
self | ) |
|
Returns Collective expression containing all the control space gradients for all control variable types (fields).
Notes:
1. It expects that the CalculateValue is called.
2. The gradients are computed with respect to updates from master control.
Returns:
KratosOA.CollectiveExpression: Returns mapped gradients collective expression.
◆ CalculateValue()
| float responses.response_routine.ResponseRoutine.CalculateValue |
( |
|
self, |
|
|
KratosOA.CollectiveExpression |
control_field |
|
) |
| |
Calculates the value of the response.
This method updates the design with the provided control field. If a control field is updated
which affects the this response value, then a new value is computed. Otherwise, the previous
value is returned.
Args:
control_field (KratosOA.CollectiveExpression): Control field of the new design.
Returns:
float: Respone value.
◆ Check()
| def responses.response_routine.ResponseRoutine.Check |
( |
|
self | ) |
|
◆ Finalize()
| def responses.response_routine.ResponseRoutine.Finalize |
( |
|
self | ) |
|
◆ GetMasterControl()
| MasterControl responses.response_routine.ResponseRoutine.GetMasterControl |
( |
|
self | ) |
|
◆ GetReponse()
| ResponseFunction responses.response_routine.ResponseRoutine.GetReponse |
( |
|
self | ) |
|
◆ GetRequiredPhysicalGradients()
| 'dict[SupportedSensitivityFieldVariableTypes, KratosOA.CollectiveExpression]' responses.response_routine.ResponseRoutine.GetRequiredPhysicalGradients |
( |
|
self | ) |
|
Returns required physical gradients by this response.
This method returns required physical gradients. The expressions may or not be empty field
depending on CalculateGradient is called or not.
Returns:
dict[SupportedSensitivityFieldVariableTypes, KratosOA.CollectiveExpression]: Required physical gradients.
◆ Initialize()
| def responses.response_routine.ResponseRoutine.Initialize |
( |
|
self | ) |
|
Initializes the response routine.
Raises:
RuntimeError: If control domain and response domain does not have any intersection.
The documentation for this class was generated from the following file:
- /home/runner/work/Documentation/Documentation/master/applications/OptimizationApplication/python_scripts/responses/response_routine.py


XGBoost is short for eXtreme Gradient Boosting package.
The purpose of this Vignette is to show you how to use XGBoost to build a model and make predictions.
It is an efficient and scalable implementation of gradient boosting framework by J. Friedman et al. (2000) and J. H. Friedman (2001). Two solvers are included:
It supports various objective functions, including regression, classification and ranking. The package is made to be extendible, so that users are also allowed to define their own objective functions easily.
It has been used to win several Kaggle competitions.
It has several features:
gbm
.matrix
;Matrix::dgCMatrix
;xgb.DMatrix
: its own class (recommended).For weekly updated version (highly recommended), install from GitHub:
install.packages("drat", repos="https://cran.rstudio.com")
:::addRepo("dmlc")
dratinstall.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
Windows user will need to install Rtools first.
The version 0.4-2 is on CRAN, and you can install it by:
install.packages("xgboost")
Formerly available versions can be obtained from the CRAN archive
For the purpose of this tutorial we will load XGBoost package.
require(xgboost)
In this example, we are aiming to predict whether a mushroom can be eaten or not (like in many tutorials, example data are the same as you will use on in your every day life :-).
Mushroom data is cited from UCI Machine Learning Repository. Bache and Lichman (2013).
We will load the agaricus
datasets embedded with the
package and will link them to variables.
The datasets are already split in:
train
: will be used to build the model ;test
: will be used to assess the quality of our
model.Why split the dataset in two parts?
In the first part we will build our model. In the second part we will want to test it and assess its quality. Without dividing the dataset we would test the model on the data which the algorithm have already seen.
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
<- agaricus.train
train <- agaricus.test test
In the real world, it would be up to you to make this division between
train
andtest
data. The way to do it is out of the purpose of this article, howevercaret
package may help.
Each variable is a list
containing two things,
label
and data
:
str(train)
## List of 2
## $ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. ..@ i : int [1:143286] 2 6 8 11 18 20 21 24 28 32 ...
## .. ..@ p : int [1:127] 0<PHONE_NUMBER> 5845 6489 6513 8380 8384 10991 ...
## .. ..@ Dim : int [1:2] 6513 126
## .. ..@ Dimnames:List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:126] "cap-shape=bell" "cap-shape=conical" "cap-shape=convex" "cap-shape=flat" ...
## .. ..@ x : num [1:143286] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..@ factors : list()
## $ label: num [1:6513] 1 0 0 1 0 0 0 1 0 0 ...
label
is the outcome of our dataset meaning it is the
binary classification we will try to predict.
Let’s discover the dimensionality of our datasets.
dim(train$data)
## [1] 6513 126
dim(test$data)
## [1] 1611 126
This dataset is very small to not make the R package too heavy, however XGBoost is built to manage huge dataset very efficiently.
As seen below, the data
are stored in a
dgCMatrix
which is a sparse matrix and
label
vector is a numeric
vector
({0,1}
):
class(train$data)[1]
## [1] "dgCMatrix"
class(train$label)
## [1] "numeric"
This step is the most critical part of the process for the quality of our model.
We are using the train
data. As explained above, both
data
and label
are stored in a
list
.
In a sparse matrix, cells containing 0
are not
stored in memory. Therefore, in a dataset mainly made of 0
,
memory size is reduced. It is very usual to have such dataset.
We will train decision tree model using the following parameters:
objective = "binary:logistic"
: we will train a binary
classification model ;max_depth = 2
: the trees won’t be deep, because our
case is very simple ;nthread = 2
: the number of CPU threads we are going to
use;nrounds = 2
: there will be two passes on the data, the
second one will enhance the model by further reducing the difference
between ground truth and prediction.<- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic") bstSparse
## [1] train-logloss:0.233376
## [2] train-logloss:0.136658
More complex the relationship between your features and your
label
is, more passes you need.
Alternatively, you can put your dataset in a dense matrix, i.e. a basic R matrix.
<- xgboost(data = as.matrix(train$data), label = train$label, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic") bstDense
## [1] train-logloss:0.233376
## [2] train-logloss:0.136658
XGBoost offers a way to group them in a
xgb.DMatrix
. You can even add other meta data in it. It
will be useful for the most advanced features we will discover
later.
<- xgb.DMatrix(data = train$data, label = train$label)
dtrain <- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic") bstDMatrix
## [1] train-logloss:0.233376
## [2] train-logloss:0.136658
XGBoost has several features to help you to view how the learning progress internally. The purpose is to help you to set the best parameters, which is the key of your model quality.
One of the simplest way to see the training progress is to set the
verbose
option (see below for more advanced
techniques).
# verbose = 0, no message
<- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0) bst
# verbose = 1, print evaluation metric
<- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 1) bst
## [1] train-logloss:0.233376
## [2] train-logloss:0.136658
# verbose = 2, also print information about tree
<- xgboost(data = dtrain, max_depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 2) bst
## [1] train-logloss:0.233376
## [2] train-logloss:0.136658
The purpose of the model we have built is to classify new data. As
explained before, we will use the test
dataset for this
step.
<- predict(bst, test$data)
pred
# size of the prediction vector
print(length(pred))
## [1] 1611
# limit display of predictions to the first 10
print(head(pred))
## [1] 0.28583017 0.92392391 0.28583017 0.28583017 0.05169873 0.92392391
These numbers doesn’t look like binary classification
{0,1}
. We need to perform a simple transformation before
being able to use these results.
The only thing that XGBoost does is a
regression. XGBoost is using
label
vector to build its regression model.
How can we use a regression model to perform a binary classification?
If we think about the meaning of a regression applied to our data,
the numbers we get are probabilities that a datum will be classified as
1
. Therefore, we will set the rule that if this probability
for a specific datum is > 0.5
then the observation is
classified as 1
(or 0
otherwise).
<- as.numeric(pred > 0.5)
prediction print(head(prediction))
## [1] 0 1 0 0 0 1
To measure the model performance, we will compute a simple metric, the average error.
<- mean(as.numeric(pred > 0.5) != test$label)
err print(paste("test-error=", err))
## [1] "test-error= 0.0217256362507759"
Note that the algorithm has not seen the
test
data during the model construction.
Steps explanation:
as.numeric(pred > 0.5)
applies our rule that when
the probability (<=> regression <=> prediction) is
> 0.5
the observation is classified as 1
and 0
otherwise ;probabilityVectorPreviouslyComputed != test$label
computes the vector of error between true data and computed
probabilities ;mean(vectorOfErrors)
computes the average
error itself.The most important thing to remember is that to do a
classification, you just do a regression to the
label
and then apply a threshold.
Multiclass classification works in a similar way.
This metric is 0.02 and is pretty low: our yummy mushroom model works well!
Most of the features below have been implemented to help you to improve your model by offering a better understanding of its content.
For the following advanced features, we need to put data in
xgb.DMatrix
as explained above.
<- xgb.DMatrix(data = train$data, label=train$label)
dtrain <- xgb.DMatrix(data = test$data, label=test$label) dtest
Both xgboost
(simple) and xgb.train
(advanced) functions train models.
One of the special feature of xgb.train
is the capacity
to follow the progress of the learning after each round. Because of the
way boosting works, there is a time when having too many rounds lead to
an overfitting. You can see this feature as a cousin of cross-validation
method. The following techniques will help you to avoid overfitting or
optimizing the learning time in stopping it as soon as possible.
One way to measure progress in learning of a model is to provide to XGBoost a second dataset already classified. Therefore it can learn on the first dataset and test its model on the second one. Some metrics are measured after each round during the learning.
in some way it is similar to what we have done above with the average error. The main difference is that below it was after building the model, and now it is during the construction that we measure errors.
For the purpose of this example, we use watchlist
parameter. It is a list of xgb.DMatrix
, each of them tagged
with a name.
<- list(train=dtrain, test=dtest)
watchlist
<- xgb.train(data=dtrain, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic") bst
## [1] train-logloss:0.233376 test-logloss:0.226686
## [2] train-logloss:0.136658 test-logloss:0.137874
XGBoost has computed at each round the same average
error metric than seen above (we set nrounds
to 2, that is
why we have two lines). Obviously, the train-error
number
is related to the training dataset (the one the algorithm learns from)
and the test-error
number to the test dataset.
Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.
If with your own dataset you have not such results, you should think
about how you divided your dataset in training and test. May be there is
something to fix. Again, caret
package may help.
For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.
<- xgb.train(data=dtrain, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, eval_metric = "error", eval_metric = "logloss", objective = "binary:logistic") bst
## [1] train-error:0.046522 train-logloss:0.233376 test-error:0.042831 test-logloss:0.226686
## [2] train-error:0.022263 train-logloss:0.136658 test-error:0.021726 test-logloss:0.137874
eval_metric
allows us to monitor two new metrics for each round,logloss
anderror
.
Until now, all the learnings we have performed were based on boosting
trees. XGBoost implements a second algorithm, based on
linear boosting. The only difference with previous command is
booster = "gblinear"
parameter (and removing
eta
parameter).
<- xgb.train(data=dtrain, booster = "gblinear", max_depth=2, nthread = 2, nrounds=2, watchlist=watchlist, eval_metric = "error", eval_metric = "logloss", objective = "binary:logistic") bst
## [02:10:38] WARNING: src/learner.cc:767:
## Parameters: { "max_depth" } are not used.
##
## [1] train-error:0.014893 train-logloss:0.191250 test-error:0.016760 test-logloss:0.193889
## [2] train-error:0.004760 train-logloss:0.083530 test-error:0.003724 test-logloss:0.085053
In this specific case, linear boosting gets slightly better performance metrics than decision trees based algorithm.
In simple cases, it will happen because there is nothing better than a linear algorithm to catch a linear link. However, decision trees are much better to catch a non linear link between predictors and outcome. Because there is no silver bullet, we advise you to check both algorithms with your own datasets to have an idea of what to use.
Like saving models, xgb.DMatrix
object (which groups
both dataset and outcome) can also be saved using
xgb.DMatrix.save
function.
xgb.DMatrix.save(dtrain, "dtrain.buffer")
## [1] TRUE
# to load it in, simply call xgb.DMatrix
<- xgb.DMatrix("dtrain.buffer") dtrain2
## [02:10:38] 6513x126 matrix with 143286 entries loaded from dtrain.buffer
<- xgb.train(data=dtrain2, max_depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic") bst
## [1] train-logloss:0.233376 test-logloss:0.226686
## [2] train-logloss:0.136658 test-logloss:0.137874
Information can be extracted from xgb.DMatrix
using
getinfo
function. Hereafter we will extract
label
data.
= getinfo(dtest, "label")
label <- predict(bst, dtest)
pred <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
err print(paste("test-error=", err))
## [1] "test-error= 0.0217256362507759"
Feature importance is similar to R gbm package’s relative influence (rel.inf).
importance_matrix <- xgb.importance(model = bst)
print(importance_matrix)
xgb.plot.importance(importance_matrix = importance_matrix)
You can dump the tree you learned using xgb.dump
into a
text file.
xgb.dump(bst, with_stats = TRUE)
## [1] "booster[0]"
## [2] "0:[f28<-9.53674316e-07] yes=1,no=2,missing=1,gain=4000.53101,cover=1628.25"
## [3] "1:[f55<-9.53674316e-07] yes=3,no=4,missing=3,gain=1158.21204,cover=924.5"
## [4] "3:leaf=1.71217716,cover=812"
## [5] "4:leaf=-1.70044053,cover=112.5"
## [6] "2:[f108<-9.53674316e-07] yes=5,no=6,missing=5,gain=198.173828,cover=703.75"
## [7] "5:leaf=-1.94070864,cover=690.5"
## [8] "6:leaf=1.85964918,cover=13.25"
## [9] "booster[1]"
## [10] "0:[f59<-9.53674316e-07] yes=1,no=2,missing=1,gain=832.544983,cover=788.852051"
## [11] "1:[f28<-9.53674316e-07] yes=3,no=4,missing=3,gain=569.725098,cover=768.389709"
## [12] "3:leaf=0.78471756,cover=458.936859"
## [13] "4:leaf=-0.968530357,cover=309.45282"
## [14] "2:leaf=-6.23624468,cover=20.462389"
You can plot the trees from your model using
`xgb.plot.tree
xgb.plot.tree(model = bst)
if you provide a path to
fname
parameter you can save the trees to your hard drive.
Maybe your dataset is big, and it takes time to train a model on it? May be you are not a big fan of losing time in redoing the same task again and again? In these very rare cases, you will want to save your model and load it when required.
Hopefully for you, XGBoost implements such functions.
# save model to binary local file
xgb.save(bst, "xgboost.model")
## [1] TRUE
xgb.save
function should return TRUE if everything goes well and crashes otherwise.
An interesting test to see how identical our saved model is to the original one would be to compare the two predictions.
# load binary model to R
<- xgb.load("xgboost.model")
bst2 <- predict(bst2, test$data)
pred2
# And now the test
print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
## [1] "sum(abs(pred2-pred))= 0"
result is
0
? We are good!
In some very specific cases, like when you want to pilot
XGBoost from caret
package, you will want
to save the model as a R binary vector. See below how to do
it.
# save model to R's raw vector
<- xgb.serialize(bst)
rawVec
# print class
print(class(rawVec))
## [1] "raw"
# load binary model to R
<- xgb.load(rawVec)
bst3 <- predict(bst3, test$data)
pred3
# pred2 should be identical to pred
print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
## [1] "sum(abs(pred3-pred))= 0"
Again
0
? It seems thatXGBoost
works pretty well!


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This is the clicking challenge
Player1
In the lead:
Player2
Play with:
Keyboard
Mouse
Background Color
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Duration
15 minutes
Goals
In this group exercise, we will identify a memory leak in a Xamarin.iOS application which is due to a user-peer reference cycle.
Assets
The provided Exercise 3 folder contains a sub-folder named Start with a pre-built Xamarin.iOS application which has a strong-reference cycle in it due to a user peer relationship.
Steps
Below are the step-by-step instructions to find and fix the reference cycle.
Run the application to see the issue
Let's start by familiarizing ourselves with the starter solution.
- Open the UserPeers.sln solution.
- Run the application to see what it does. It should show a blank screen with a toolbar with three buttons:
- "+" to add a custom view onto the screen.
- "X" to remove the custom view from the screen.
- "Trash" to perform a GC.
- If you press the "+" button, you will get a new view added to the screen:
- You can tap on the rectangle to get it to change the color.
- Tapping the "X" button will remove it from the screen.
- Tapping the trash can icon will perform a GC. Notice that in the debug console you will see output as the object is created/destroyed:
- Create and destroy the view a few times - you should see the counter continuing to increase but it never goes down; we're leaking the custom view every time we add it.
CustomParentView created, 1 live instances CustomChildView created, 1 live instances Triggering GC.
Explore the source code
Next, lets look at the code for the application to see if we can identify the issue.
-
Expand the solution contents.
- It has a single main
ViewController
. which provides the main screen you interact with. - It has a Views folder which contains two custom view derived classes. Both of these report their total live instances as they are created or disposed.
- It has a single main
- The ViewController.cs class is what adds and removes the view. Check to make sure it's removing the subview and ensuring it doesn't hold a live reference.
- Next, check the parent and child views to see how the relationships are held between them. Notice that the child view holds a strong reference to the parent view so it can change the background fill color when it's touched.
- Recall from the lecture that managed to native circular references cause issues in Xamarin.iOS due to the difference in how memory is handled between Mono and iOS.
Approaches to fixing the leak.
There are multiple ways to fix this problem. Let's try each one in turn.
-
First, the easiest way to fix the problem is to force the parent to be disposed. Open the ViewController.cs source file and locate the method where we remove the subview. Add a call to
Dispose
before setting the field to null. - Run the app and see if it cleans up the view - add and remove a few subviews and force GCs to make sure the counter always ends up at zero.
-
That's an easy fix, but it requires that our client correct the issue - really the problem is in the parent/child relationship established by the custom views. Remove the call to
Dispose
and let's see if we can fix it in the child views. -
Override the
RemoveFromSuperview
method on the CustomParentView.cs class - when it's invoked, get the child from theSubviews
collection and set it'sParent
property tonull
. - Run the app again and verify that this new approach also solves the problem.
-
There is a third solution to this problem - see if you can remove the direct managed dependency from
CustomChildView
toCustomParentView
by changing how you retrieve your parent. See the final completed solution for this approach.
Summary
In this exercise, we fixed a circular reference problem with Xamarin.iOS where a parent/child relationship caused a View hierarchy to get held in memory. There is a Completed solution in the Exercise 3 folder if you'd like to see the final approach.


This block contains description of the shape interrogation facilities available in Analysis Situs. In general,
using the inspection tools, it is possible to extract the following information:
- Topological connectivity in form of graphs.
- Host geometry of edges and faces (types of curves and surfaces, their properties).
- Local tolerances, locations and orientations associated with the boundary elements.
Reverse engineering eliminates the need to design from a blank sheet. The
process takes an existing part, captures its surface measurements, and uses
this information as a reference to create the new design. Reverse engineering
techniques can contribute to solving the following tasks:
- Create CAD models for legacy parts where no digital models are available.
- Design organic shapes whose features are very difficult to reproduce without a polygonal underlayer.
- Change (optimize) design of existing product.
- Replicate existing products.
- Repair worn-out or broken parts.
- Understand better the properties of the existing parts (e.g. it symmetry, defects, etc.).